crypto_clustering1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.cluster import DBSCAN\n",
    "from sklearn.metrics import silhouette_score\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA_PATH = 'data_phase2/Q1_data.csv'\n",
    "crypto_data = pd.read_csv(DATA_PATH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>symbol</th>\n",
       "      <th>name</th>\n",
       "      <th>time_stamp</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>BTC</td>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>2023-05-17T23:59:59.999Z</td>\n",
       "      <td>2023-05-17T20:09:00.000Z</td>\n",
       "      <td>2023-05-17T14:03:00.000Z</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>BTC</td>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>2023-05-13T23:59:59.999Z</td>\n",
       "      <td>2023-05-13T18:50:00.000Z</td>\n",
       "      <td>2023-05-13T00:32:00.000Z</td>\n",
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       "      <td>5.188737e+11</td>\n",
       "      <td>9.999172e+09</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>BTC</td>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>2023-03-08T23:59:59.999Z</td>\n",
       "      <td>2023-03-08T00:23:00.000Z</td>\n",
       "      <td>2023-03-08T23:43:00.000Z</td>\n",
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       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>BTC</td>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>2023-02-21T23:59:59.999Z</td>\n",
       "      <td>2023-02-21T08:30:00.000Z</td>\n",
       "      <td>2023-02-21T22:06:00.000Z</td>\n",
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       "      <td>24436.354485</td>\n",
       "      <td>25126.851686</td>\n",
       "      <td>24200.364116</td>\n",
       "      <td>4.715777e+11</td>\n",
       "      <td>3.125210e+10</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>BTC</td>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>2022-10-15T23:59:59.999Z</td>\n",
       "      <td>2022-10-15T03:59:00.000Z</td>\n",
       "      <td>2022-10-15T23:04:00.000Z</td>\n",
       "      <td>19185.437304</td>\n",
       "      <td>19067.635082</td>\n",
       "      <td>19212.541608</td>\n",
       "      <td>19019.250125</td>\n",
       "      <td>3.657224e+11</td>\n",
       "      <td>1.619224e+10</td>\n",
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       "   Unnamed: 0 symbol     name                time_stamp  \\\n",
       "0           0    BTC  Bitcoin  2023-05-17T23:59:59.999Z   \n",
       "1           1    BTC  Bitcoin  2023-05-13T23:59:59.999Z   \n",
       "2           2    BTC  Bitcoin  2023-03-08T23:59:59.999Z   \n",
       "3           3    BTC  Bitcoin  2023-02-21T23:59:59.999Z   \n",
       "4           4    BTC  Bitcoin  2022-10-15T23:59:59.999Z   \n",
       "\n",
       "                  time_high                  time_low          open  \\\n",
       "0  2023-05-17T20:09:00.000Z  2023-05-17T14:03:00.000Z  27035.470130   \n",
       "1  2023-05-13T18:50:00.000Z  2023-05-13T00:32:00.000Z  26807.769044   \n",
       "2  2023-03-08T00:23:00.000Z  2023-03-08T23:43:00.000Z  22216.442321   \n",
       "3  2023-02-21T08:30:00.000Z  2023-02-21T22:06:00.000Z  24833.048914   \n",
       "4  2022-10-15T03:59:00.000Z  2022-10-15T23:04:00.000Z  19185.437304   \n",
       "\n",
       "          close          high           low    market_cap        volume  \n",
       "0  27398.802028  27465.927904  26600.144844  5.308901e+11  1.514001e+10  \n",
       "1  26784.078561  27030.482960  26710.873803  5.188737e+11  9.999172e+09  \n",
       "2  21718.079830  22268.896252  21708.050506  4.194214e+11  2.253658e+10  \n",
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    }
   ],
   "source": [
    "crypto_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1456 entries, 0 to 1455\n",
      "Data columns (total 12 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   Unnamed: 0  1456 non-null   int64  \n",
      " 1   symbol      1456 non-null   object \n",
      " 2   name        1456 non-null   object \n",
      " 3   time_stamp  1456 non-null   object \n",
      " 4   time_high   1456 non-null   object \n",
      " 5   time_low    1456 non-null   object \n",
      " 6   open        1456 non-null   float64\n",
      " 7   close       1456 non-null   float64\n",
      " 8   high        1456 non-null   float64\n",
      " 9   low         1456 non-null   float64\n",
      " 10  market_cap  1456 non-null   float64\n",
      " 11  volume      1456 non-null   float64\n",
      "dtypes: float64(6), int64(1), object(5)\n",
      "memory usage: 136.6+ KB\n"
     ]
    }
   ],
   "source": [
    "crypto_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
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       "      <th>Unnamed: 0</th>\n",
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       "      <th>count</th>\n",
       "      <td>1456.000000</td>\n",
       "      <td>1456.000000</td>\n",
       "      <td>1456.000000</td>\n",
       "      <td>1456.000000</td>\n",
       "      <td>1456.000000</td>\n",
       "      <td>1.456000e+03</td>\n",
       "      <td>1.456000e+03</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>727.500000</td>\n",
       "      <td>6409.803298</td>\n",
       "      <td>6413.959488</td>\n",
       "      <td>6511.671925</td>\n",
       "      <td>6310.950717</td>\n",
       "      <td>1.935885e+11</td>\n",
       "      <td>1.641040e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>420.455309</td>\n",
       "      <td>10308.820707</td>\n",
       "      <td>10315.745017</td>\n",
       "      <td>10469.614104</td>\n",
       "      <td>10154.698361</td>\n",
       "      <td>1.708323e+11</td>\n",
       "      <td>1.699314e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.997814</td>\n",
       "      <td>0.997835</td>\n",
       "      <td>0.998569</td>\n",
       "      <td>0.981525</td>\n",
       "      <td>3.240715e+10</td>\n",
       "      <td>2.609959e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>363.750000</td>\n",
       "      <td>158.186886</td>\n",
       "      <td>158.231137</td>\n",
       "      <td>159.163097</td>\n",
       "      <td>153.550839</td>\n",
       "      <td>6.316777e+10</td>\n",
       "      <td>2.451769e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>727.500000</td>\n",
       "      <td>727.263336</td>\n",
       "      <td>727.288071</td>\n",
       "      <td>765.555654</td>\n",
       "      <td>715.152641</td>\n",
       "      <td>1.092437e+11</td>\n",
       "      <td>1.204046e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1091.250000</td>\n",
       "      <td>5535.576237</td>\n",
       "      <td>5536.825389</td>\n",
       "      <td>5666.345777</td>\n",
       "      <td>5457.385796</td>\n",
       "      <td>2.661834e+11</td>\n",
       "      <td>2.449331e+10</td>\n",
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       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1455.000000</td>\n",
       "      <td>31474.721364</td>\n",
       "      <td>31476.049385</td>\n",
       "      <td>31814.514812</td>\n",
       "      <td>30659.355893</td>\n",
       "      <td>6.115354e+11</td>\n",
       "      <td>1.627349e+11</td>\n",
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       "        Unnamed: 0          open         close          high           low  \\\n",
       "count  1456.000000   1456.000000   1456.000000   1456.000000   1456.000000   \n",
       "mean    727.500000   6409.803298   6413.959488   6511.671925   6310.950717   \n",
       "std     420.455309  10308.820707  10315.745017  10469.614104  10154.698361   \n",
       "min       0.000000      0.997814      0.997835      0.998569      0.981525   \n",
       "25%     363.750000    158.186886    158.231137    159.163097    153.550839   \n",
       "50%     727.500000    727.263336    727.288071    765.555654    715.152641   \n",
       "75%    1091.250000   5535.576237   5536.825389   5666.345777   5457.385796   \n",
       "max    1455.000000  31474.721364  31476.049385  31814.514812  30659.355893   \n",
       "\n",
       "         market_cap        volume  \n",
       "count  1.456000e+03  1.456000e+03  \n",
       "mean   1.935885e+11  1.641040e+10  \n",
       "std    1.708323e+11  1.699314e+10  \n",
       "min    3.240715e+10  2.609959e+08  \n",
       "25%    6.316777e+10  2.451769e+09  \n",
       "50%    1.092437e+11  1.204046e+10  \n",
       "75%    2.661834e+11  2.449331e+10  \n",
       "max    6.115354e+11  1.627349e+11  "
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     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "crypto_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
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          "xaxis": {
           "automargin": true,
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           "ticks": "",
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           "zerolinewidth": 2
          },
          "yaxis": {
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         "text": "Market Cap vs. Volume",
         "x": 0.5
        },
        "xaxis": {
         "anchor": "y",
         "domain": [
          0,
          1
         ],
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          "text": "Market Cap ($)"
         }
        },
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         "anchor": "x",
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          0,
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          "text": "Volume ($)"
         }
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     },
     "metadata": {},
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    }
   ],
   "source": [
    "# Plot Scatter Plot of Market Cap vs. Volume\n",
    "fig = px.scatter(crypto_data, x=\"market_cap\", y=\"volume\", color=\"symbol\")\n",
    "fig.update_layout(\n",
    "    title='Market Cap vs. Volume',\n",
    "    title_x=0.5,\n",
    "    xaxis_title='Market Cap ($)',\n",
    "    yaxis_title='Volume ($)',    \n",
    ")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 dir=rtl align=right style=\"line-height:200%;font-family:vazir;color:#0099cc\">\n",
    "<font face=\"vazir\" color=\"#0099cc\">\n",
    "بخش اول\n",
    "</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run K-Means Clustering (with k = 5) on the dataset using the market cap and volume features\n",
    "kmeans = KMeans(n_clusters=5, init='k-means++', random_state=42)\n",
    "kmeans.fit(crypto_data[['market_cap', 'volume']])\n",
    "crypto_data['cluster_kmeans'] = [f'cluster {label + 1}' for label in kmeans.labels_]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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         "text": "Market Cap vs. Volume, Clussified by K-Means Clustering (k = 5)",
         "x": 0.5
        },
        "xaxis": {
         "title": {
          "text": "Market Cap ($)"
         }
        },
        "yaxis": {
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          "text": "Volume ($)"
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   ],
   "source": [
    "# Plot Scatter Plot of Market Cap vs. Volume, Clussified by K-Means Clustering (k = 5)\n",
    "fig = go.Figure()\n",
    "for cluster in crypto_data['cluster_kmeans'].sort_values().unique():\n",
    "    fig.add_trace(\n",
    "        go.Scatter(\n",
    "            x=crypto_data[crypto_data['cluster_kmeans'] == cluster]['market_cap'], \n",
    "            y=crypto_data[crypto_data['cluster_kmeans'] == cluster]['volume'],\n",
    "            mode='markers', \n",
    "            name=cluster,\n",
    "            marker=dict(opacity=0.7)))\n",
    "\n",
    "centers = kmeans.cluster_centers_\n",
    "fig.add_trace(go.Scatter(\n",
    "    x=centers[:, 0],\n",
    "    y=centers[:, 1],\n",
    "    mode='markers',\n",
    "    marker=dict(color='Black', size=6, line=dict(color='Black', width=2)),\n",
    "    name='Centers'\n",
    "))\n",
    "\n",
    "fig.update_layout(\n",
    "    title='Market Cap vs. Volume, Clussified by K-Means Clustering (k = 5)',\n",
    "    title_x=0.5,\n",
    "    xaxis_title='Market Cap ($)',\n",
    "    yaxis_title='Volume ($)',    \n",
    ")\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 dir=rtl align=right style=\"line-height:200%;font-family:vazir;color:#0099cc\">\n",
    "<font face=\"vazir\" color=\"#0099cc\">\n",
    "بخش دوم\n",
    "</font>"
   ]
  },
  {
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    "# Find the optimal number of clusters using the elbow method\n",
    "wcss = []\n",
    "for k in range(1, 11):\n",
    "    kmeans = KMeans(n_clusters=k, init='k-means++', random_state=42)\n",
    "    kmeans.fit(crypto_data[['market_cap', 'volume']])\n",
    "    wcss.append(kmeans.inertia_)\n",
    "\n",
    "fig = px.line(x=range(1, 11), y=wcss, markers='o')\n",
    "fig.update_layout(\n",
    "    title='Elbow Method',\n",
    "    title_x=0.5,\n",
    "    xaxis_title='Number of Clusters',\n",
    "    yaxis_title='Within-Cluster Sum of Square',    \n",
    ")\n",
    "fig.update_xaxes(nticks=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best k: 4\n"
     ]
    }
   ],
   "source": [
    "silhouette_scores = []\n",
    "for k in range(2, 10):\n",
    "    kmeans = KMeans(n_clusters=k, init='k-means++', random_state=42)\n",
    "    cluster_labels = kmeans.fit_predict(crypto_data[['market_cap', 'volume']])  # Replace 'data' with your dataset\n",
    "    silhouette_avg = silhouette_score(crypto_data[['market_cap', 'volume']], cluster_labels)\n",
    "    silhouette_scores.append(silhouette_avg)\n",
    "\n",
    "best_k = silhouette_scores.index(max(silhouette_scores)) + 2\n",
    "print(f'Best k: {best_k}')"
   ]
  },
  {
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   "execution_count": 11,
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   ],
   "source": [
    "# Run K-Means Clustering again, this time for best k(=4) on the dataset using the market cap and volume features\n",
    "kmeans_best = KMeans(n_clusters=best_k, init='k-means++', random_state=42)\n",
    "kmeans_best.fit(crypto_data[['market_cap', 'volume']])\n",
    "crypto_data['cluster_kmeans (best k)'] = [f'cluster {label + 1}' for label in kmeans_best.labels_]\n",
    "\n",
    "# Plot Scatter Plot of Market Cap vs. Volume, Clussified by K-Means Clustering using best k(= 4)\n",
    "fig = go.Figure()\n",
    "for cluster in crypto_data['cluster_kmeans (best k)'].sort_values().unique():\n",
    "    fig.add_trace(\n",
    "        go.Scatter(\n",
    "            x=crypto_data[crypto_data['cluster_kmeans (best k)'] == cluster]['market_cap'], \n",
    "            y=crypto_data[crypto_data['cluster_kmeans (best k)'] == cluster]['volume'],\n",
    "            mode='markers', \n",
    "            name=cluster,\n",
    "            marker=dict(opacity=0.7)))\n",
    "\n",
    "centers = kmeans_best.cluster_centers_\n",
    "fig.add_trace(go.Scatter(\n",
    "    x=centers[:, 0],\n",
    "    y=centers[:, 1],\n",
    "    mode='markers',\n",
    "    marker=dict(color='Black', size=6, line=dict(color='Black', width=2)),\n",
    "    name='Centers'\n",
    "))\n",
    "\n",
    "fig.update_layout(\n",
    "    title='Market Cap vs. Volume, Clussified by K-Means Clustering, for best k(= 4)',\n",
    "    title_x=0.5,\n",
    "    xaxis_title='Market Cap ($)',\n",
    "    yaxis_title='Volume ($)',    \n",
    ")\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p dir=rtl style=\"direction: rtl;text-align: center;line-height:200%;font-family:vazir;font-size:medium;color:#0099cc\"><font face=\"vazir\" size=3><i>\n",
    "[تحلیل] برای بدست آوردن مقدار بهینه k از دو روش elbow  و silhouette بهره بردیم. همان طور که در نمودار elbow می توان مشاهده کرد،‌ اگر مقدار k را بزرگتر از ۴ قرار دهیم، تغییر قابل توجهی در مقدار خطای WCSS مشاهده نمی شود؛ بنابراین می توان نتیجه گرفت تعداد خوشه بهینه برای این مجموعه داده برابر با ۴ است و روش sihouette هم در ادامه این تعداد خوشه را تایید می کند. \n",
    "اما باید پرسید «آیا ۴ خوشه برای این مجموعه داده تفسیر پذیر است؟» در نگاه اول این طور به نظر می رسد،‌ چرا که داده های ما اصلا مربوط به ۴ نوع رمزارز هستند.  اما اگر دوباره الگوریتم را با k = 4 اجرا کنیم،‌ مشاهده می کنیم که الگوریتم داده های مربوط به دو رمزارز (BNB و USDT)  را در یک خوشه قرار می دهد و از طرف دیگر، داده های مربوط به رمز ارز بیت کوین را در دو خوشه قرار می دهد. ما ظاهرا،‌ در نگاهی ساده و اولیه، انتظار داشتیم که اصطلاحا الگوریتم داده ها را از مفصل هایش ببرد و داده های  مربوط به هر رمز ارز را در یک خوشه مجزا طبقه بندی کند. باید پرسید اینکه داده های بیت کوین در دو خوشه طبقه بندی می شوند چه تفسیری دارد؟ و این که چرا دو رمز ارز BNB و USDT در یک خوشه قرار می گیرند؟ شاید اگر یک یا چند بعد دیگر از به داده هایمان اضافه کنیم،‌ داده های مربوط به هر رمز ارز دقیقا در یک خوشه طبقه بندی شوند.\n",
    "\n",
    "</i></font></p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 dir=rtl align=right style=\"line-height:200%;font-family:vazir;color:#0099cc\">\n",
    "<font face=\"vazir\" color=\"#0099cc\">\n",
    "بخش سوم\n",
    "</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of clusters found: 5\n",
      "Number of noises found: 20\n"
     ]
    }
   ],
   "source": [
    "# Run DBSCAN Clustering on the dataset using the market cap and volume features\n",
    "market_caps_scaled = crypto_data['market_cap'].values / 4\n",
    "volumes = crypto_data['volume'].values\n",
    "dbscan = DBSCAN(eps=0.5e10 + 0.7e9 + 0.5e8, min_samples=4)\n",
    "dbscan.fit(np.stack((market_caps_scaled, volumes), axis=-1))\n",
    "crypto_data['cluster_dbscan'] = [\n",
    "    f'cluster {label + 1}' if label != -1 else 'noise' for label in dbscan.labels_\n",
    "]\n",
    "n_clusters = len(set(dbscan.labels_)) - (1 if -1 in dbscan.labels_ else 0)\n",
    "n_noises = len([label for label in dbscan.labels_ if label == -1])\n",
    "print(f'Number of clusters found: {n_clusters}')\n",
    "\n",
    "print(f'Number of noises found: {n_noises}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
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        },
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      }
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     "metadata": {},
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   ],
   "source": [
    "# Plot Scatter Plot of Market Cap vs. Volume, Clussified by DBSCAN Clustering\n",
    "fig = go.Figure()\n",
    "\n",
    "clusters_colors = px.colors.qualitative.Plotly\n",
    "for i, cluster in enumerate(crypto_data['cluster_dbscan'].unique()):\n",
    "    if cluster == 'noise':\n",
    "        cluster_color = 'red'\n",
    "    else:\n",
    "        cluster_color = clusters_colors[i + 4]\n",
    "    fig.add_trace(\n",
    "        go.Scatter(\n",
    "            x=crypto_data[crypto_data['cluster_dbscan'] == cluster]['market_cap'], \n",
    "            y=crypto_data[crypto_data['cluster_dbscan'] == cluster]['volume'],\n",
    "            mode='markers', \n",
    "            name=cluster,\n",
    "            marker=dict(opacity=0.8, color=cluster_color)))\n",
    "\n",
    "fig.update_layout(\n",
    "    title='Market Cap vs. Volume, Clussified by DBSCAN Clustering',\n",
    "    title_x=0.5,\n",
    "    xaxis_title='Market Cap ($)',\n",
    "    yaxis_title='Volume ($)',    \n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p dir=rtl style=\"direction: rtl;text-align: center;line-height:200%;font-family:vazir;font-size:medium;color:#0099cc\"><font face=\"vazir\" size=3><i>\n",
    "[تحلیل]  الگوریتم DBSCAN حساس به دو پارامتر min_samples  و پارامتر eps (شعاع) است و هر چه eps را کوچکتر در نظر بگیریم، خوشه‌های بیشتر و کوچکتری تشکیلی می‌شود. همچنین هر چه قدر min_samples را بزرگ‌تر در نظر بگیریم، احتمال ایجادِ خوشه‌ها، کمتر می‌شود زیرا الگوریتم باید تعداد نمونه‌های بیشتری را در یک شعاع خاص ببینید تا خوشه را تشکیل دهد.\n",
    "</i></font></p>"
   ]
  }
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